WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Book Part Citation - WoS: 1Citation - Scopus: 4Understanding Communication via Diffusion: Simulation Design and Intricacies(Springer International Publishing AG, 2017) Acar, Bilal; Akkaya, Ali; Genc, Gaye; Yilmaz, H. Birkan; Kuran, M. Sukru; Tugcu, Tuna; Şükrü Kuran, M.Understanding Communication via Diffusion (CvD) is key to molecular communications research since it dominates the movement at the nano-scale. The researcher needs to properly understand the random diffusion of the molecules for the analysis of a molecular communication system. This chapter aims explaining the dynamics of diffusion from a communication engineer's perspective as well as providing useful hints for an effective simulation design by discussing some key intricacies. The chapter starts with a brief survey of simulators for molecular communications, followed by the basics of the simulation of Brownian motion and CvD. Several intricacies are addressed to help the researcher in simulation design, such as the number of replications required in terms of movement and bit sequence. We utilize this information further by discussing the design of more complex CvD systems such as tunnel-based approach that utilizes destroyer molecules and distributed simulator design based on HLA. Introduction of more complex CvD systems provides significant improvements in data rate and communications in general, bridging the gap between human-scale and nano-scale systems and enabling nanonetworking as a viable technology.Conference Object Citation - WoS: 1Citation - Scopus: 1Textnettopics-SFTS-SBTS Textnettopics Scoring Approaches Based Sequential Forward and Backward(Springer International Publishing AG, 2024) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, MalikTextNetTopics is a text classification-based topic modeling approach that performs topic selection rather than word selection to train a machine learning algorithm. However, one main limitation of TextNetTopics is that its scoring component (the S component) assesses each topic independently and ranks them accordingly, neglecting the potential relationship between topics. In order to address this limitation and improve the classification performance, this study introduces an enhancement to TextNetTopics. TextNetTopics-SFTS-SBTS integrates two novel scoring approaches: Sequential Forward Topic Scoring (SFTS) and Sequential Backward Topic Scoring (SBTS), which consider topic interactions by assessing sets of topics simultaneously. This integration aims to streamline the topic selection process and enhance classifier efficiency for text classification. The results obtained across three datasets offer valuable insights into the context-dependent effectiveness of the new scoring mechanisms across diverse datasets and varying numbers of topics involved in the analysis.Conference Object Text Classification Experiments on Contextual Graphs Built by N-Gram Series(Springer International Publishing AG, 2025) Sen, Tarik Uveys; Yakit, Mehmet Can; Gumus, Mehmet Semih; Abar, Orhan; Bakal, GokhanTraditional n-gram textual features, commonly employed in conventional machine learning models, offer lower performance rates on high-volume datasets compared to modern deep learning algorithms, which have been intensively studied for the past decade. The main reason for this performance disparity is that deep learning approaches handle textual data through the word vector space representation by catching the contextually hidden information in a better way. Nonetheless, the potential of the n-gram feature set to reflect the context is open to further investigation. In this sense, creating graphs using discriminative ngram series with high classification power has never been fully exploited by researchers. Hence, the main goal of this study is to contribute to the classification power by including the long-range neighborhood relationships for each word in the word embedding representations. To achieve this goal, we transformed the textual data by employing n-gram series into a graph structure and then trained a graph convolution network model. Consequently, we obtained contextually enriched word embeddings and observed F1-score performance improvements from 0.78 to 0.80 when we integrated those convolution-based word embeddings into an LSTM model. This research contributes to improving classification capabilities by leveraging graph structures derived from discriminative n-gram series.Conference Object TextNetTopics+: Enhancing Text Classification Through Classifier Diversity and Model Ensembling(Springer International Publishing AG, 2025) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, MalikTextNetTopics is an innovative text classification framework that integrates topic modeling with feature selection to improve model accuracy and interpretability. Unlike traditional methods that rely on individual words, TextNetTopics selects cohesive topics extracted via Latent Dirichlet Allocation as features for document representation, effectively reducing dimensionality while preserving the semantic structure of the text. This study evaluates the performance of TextNetTopics utilizing multiple machine learning algorithms in the M (Modeling) component, including Random Forest, Support Vector Machine, Gradient Boosting, eXtreme Gradient Boosting, and Logistic Regression. To further enhance classification performance, we introduce TextNetTopics+, an ensemblebased extension that leverages both hard voting and soft voting mechanisms to combine the strengths of multiple classifiers. Comprehensive experiments on the LitCovid and WOS datasets demonstrate that ensemble learning in TextNetTopics + significantly outperforms individual classifiers in TextNetTopics, confirming its effectiveness in improving model robustness and generalization.Conference Object Temporal Logic-Based Intrusion Detection for Securing Connected Vehicles(Springer International Publishing AG, 2024) Bozdal, MehmetEnsuring the security and integrity of in-vehicle communication networks (IVCNs) is paramount. The increasing connectivity of vehicles exposes them to unprecedented security vulnerabilities, necessitating innovative methodologies to safeguard against cyberattacks and unauthorized access. This research presents a novel approach to enhance IVCN security through the deployment of a Signal Temporal Logic (STL)-based Intrusion Detection System (IDS). Considering the limited resources of Electronic Control Units (ECUs), this approach offers an adaptive and lightweight solution that addresses the unique challenges posed by the dynamic nature of vehicular networks. The proposed STL-based IDS effectively detects a broad spectrum of intrusions while maintaining acceptable overhead for resource-constrained ECUs, thanks to its distributed architecture. Comprehensive experimental evaluations demonstrate significant performance improvements in detecting Denial of Service (DoS) attacks, achieving the highest accuracy of 0.996 and recall of 1.000. The system also excels in detecting fuzzy attacks, with the highest accuracy of 0.996.Conference Object Citation - Scopus: 1Semant - Feature Group Selection Utilizing Fasttext-Based Semantic Word Grouping, Scoring, and Modeling Approach for Text Classification(Springer International Publishing AG, 2024) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, MalikText classification presents a challenge due to its high-dimensional feature space. As such, devising an effective feature selection scheme is essential. In this study, we present SEMANT, a novel hybrid filter-wrapper feature selection method that utilizes filter-based Chi-Square and the wrapper-based G-S-M approach. SEMANT incorporates fastText neural word embedding similarities to promote greater semantic inclusion in the selection of features for text classification tasks. The performance of the proposed method was investigated on the WOS-5736 and LitCovid datasets and compared with TextNetTopics, a topic modeling-based topic selection algorithm for text classification. Experimental results confirm that the proposed approach outperforms its alternative.Conference Object Citation - WoS: 8Citation - Scopus: 12SVM-RCE-R Optimization of Scoring Function for SVM-RCE(Springer International Publishing AG, 2021) Yousef, Malik; Jabeer, Amhar; Bakir-Gungor, BurcuGene expression data classification provides a challenge in classification due to it having high dimensionality and a relatively small sample size. Different feature selection approaches have been used to overcome this issue and SVM-RCE being one of the more successful approach. This study is a continuation of two previous research studies SVM-RCE and SVM-RCE-R. SVM-RCE-R suggests a new approach in the scoring function for the clusters, showing that for some different combination of weights the performance was improved. The aim of this study is to find the optimal weights for the scoring function suggested in the study of SVM-RCE-R using optimization approaches. We have discovered that finding the optimal weights for the scoring function would improve the performance of the SVM-RCE-in most cases. We have shown that in some cases the performance is increased dramatically by 10% in terms of accuracy and AUC. By increasing the performance of the algorithm, it is more likely that we can extract subset genes relating to the class association of a microarray sample.Article Citation - WoS: 5Citation - Scopus: 5Rings With Modules Having a Restricted Injectivity Domain(Springer International Publishing AG, 2019-09-30) Demirci, Yilmaz Mehmet; Turkmen, Burcu Nisanci; Turkmen, Ergul; Nişancı Türkmen, BurcuWe introduce modules whose injectivity domains are contained in the class of modules with zero radical and call them working-class. This notion gives a generalization of poor modules that have minimal injectivity domain. Semisimple working-class modules always exist for arbitrary rings whereas their predecessors do not. We investigate the rings over which every module is either injective or working-class. Right weakly V-rings are examples of these rings. Moreover, we study the existence of working-class simple modules and show that if there is a projective working-class simple right module, then the ring is a right GV-ring.Conference Object Normal Mixture Model-Based Clustering of Data Using Genetic Algorithm(Springer International Publishing AG, 2020) Gogebakan, Maruf; Erol, HamzaIn this study, a new algorithm was developed for clustering multivariate big data. Normal mixture distributions are used to determine the partitions of variables. Normal mixture models obtained from the partitions of variables are generated using Genetic Algorithms (GA). Each partition in the variables corresponds to a clustering center in the normal mixture model. The best model that fits the data structure from normal mixture models is obtained by using the information criteria obtained from normal mixture distributions.Conference Object Citation - WoS: 4Citation - Scopus: 7Neurosec: FPGA-Based Neuromorphic Audio Security(Springer International Publishing AG, 2024) Isik, Murat; Vishwamith, Hiruna; Sur, Yusuf; Inadagbo, Kayode; Dikmen, I. CanNeuromorphic systems, inspired by the complexity and functionality of the human brain, have gained interest in academic and industrial attention due to their unparalleled potential across a wide range of applications. While their capabilities herald innovation, it is imperative to underscore that these computational paradigms, analogous to their traditional counterparts, are not impervious to security threats. Although the exploration of neuromorphic methodologies for image and video processing has been rigorously pursued, the realm of neuromorphic audio processing remains in its early stages. Our results highlight the robustness and precision of our FPGA-based neuromorphic system. Specifically, our system showcases a commendable balance between desired signal and background noise, efficient spike rate encoding, and unparalleled resilience against adversarial attacks such as FGSM and PGD. A standout feature of our framework is its detection rate of 94%, which, when compared to other methodologies, underscores its greater capability in identifying and mitigating threats within 5.39 dB, a commendable SNR ratio. Furthermore, neuromorphic computing and hardware security serve many sensor domains in mission-critical and privacy-preserving applications.